Idiosyncratic choice bias naturally emerges from intrinsic stochasticity in neuronal dynamics

Lior Lebovich (), Ran Darshan, Yoni Lavi, David Hansel and Yonatan Loewenstein
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Lior Lebovich: The Hebrew University
Ran Darshan: The Hebrew University
Yoni Lavi: The Hebrew University
David Hansel: Learning and Memory Lab; CNRS-UMR8002, 45 Rue des Saints Pères
Yonatan Loewenstein: The Hebrew University

Nature Human Behaviour, 2019, vol. 3, issue 11, 1190-1202

Abstract: Abstract Idiosyncratic tendency to choose one alternative over others in the absence of an identified reason is a common observation in two-alternative forced-choice experiments. Here we quantify idiosyncratic choice biases in a perceptual discrimination task and a motor task. We report substantial and significant biases in both cases that cannot be accounted for by the experimental context. Then, we present theoretical evidence that even in an idealized experiment, in which the settings are symmetric, idiosyncratic choice bias is expected to emerge from the dynamics of competing neuronal networks. We thus argue that idiosyncratic choice bias reflects the microscopic dynamics of choice and therefore is virtually inevitable in any comparison or decision task.

Date: 2019
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DOI: 10.1038/s41562-019-0682-7

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